Application of Mu in vitro transposition for high-precision mapping of protein–protein interfaces on a yeast two-hybrid platform

High-precision mapping of regions involved in protein–protein interfaces of interacting protein partners is an essential component on a path to understand various cellular functions. Transposon-based systems, particularly those involving in vitro reactions, offer exhaustive insertion mutant libraries and high-throughput platforms for many types of genetic analyses. We present here a genetic strategy to accurately map interacting protein regions at amino acid precision that is based on transposition-assisted construction, sampling, and analysis of a comprehensive insertion mutant library. The methodology integrates random pentapeptide mutagenesis of proteins, yeast two-hybrid screening, and high-resolution genetic footprinting. This straightforward strategy is general, and it provides a rapid and easy means to identify critical contact regions in proteins without the requirement of prior structural knowledge.     

Computational redesign of protein-protein interaction specificity

We developed a 'computational second-site suppressor' strategy to redesign specificity at a protein-protein interface and applied it to create new specifically interacting DNase-inhibitor protein pairs. We demonstrate that the designed switch in specificity holds in in vitro binding and functional assays. We also show that the designed interfaces are specific in the natural functional context in living cells, and present the first high-resolution X-ray crystallographic analysis of a computer-redesigned functional protein-protein interface with altered specificity. The approach should be applicable to the design of interacting protein pairs with novel specificities for delineating and re-engineering protein interaction networks in living cells.     

Mapping the human protein interactome

Interactions are the essence of all biomolecules because they cannot fulfill their roles without interacting with other molecules. Hence, mapping the interactions of biomolecules can be useful for understanding their roles and functions. Furthermore, the development of molecular based systems biology requires an understanding of the biomolecular interactions. In recent years, the mapping of protein-protein interactions in different species has been reported, but few reports have focused on the large-scale mapping of protein-protein interactions in human. Here, we review the developments in protein interaction mapping and we discuss issues and strategies for the mapping of the human protein interactome.     

Identification of the ligands of protein interaction domains through a functional approach

The identification of protein-protein interaction networks has often given important information about the functions of specific proteins and on the cross-talk among metabolic and regulatory pathways. The availability of entire genome sequences has rendered feasible the systematic screening of collections of proteins, often of unknown function, aimed to find the cognate ligands. Once identified by genetic and/or biochemical approaches, the interaction between two proteins should be validated in the physiologic environment. Herein we describe an experimental strategy to screen collections of protein-protein interaction domains to find and validate candidate interactors. The approach is based on the assumption that the overexpression in cultured cells of protein-protein interaction domains, isolated from the context of the whole protein, could titrate the endogenous ligand and, in turn, exert a dominant negative effect. The identification of the ligand could provide us with a tool to check the relevance of the interaction because the contemporary overexpression of the isolated domain and of its ligand could rescue the dominant negative phenotype. We explored this approach by analyzing the possible dominant negative effects on the cell cycle progression of a collection of phosphotyrosine binding (PTB) domains of human proteins. Of 47 PTB domains, we found that the overexpression of 10 of them significantly interfered with the cell cycle progression of NIH3T3 cells. Four of them were used as baits to identify the cognate interactors. Among these proteins, CARM1, interacting with the PTB domain of RabGAP1, and EF1alpha, interacting with RGS12, were able to rescue the block of the cell cycle induced by the isolated PTB domain of the partner protein, thus confirming in vivo the relevance of the interaction. These results suggest that the described approach can be used for the systematic screening of the ligands of various protein-protein interaction domains also by using different biological assays.     

A quantitative analysis of interfacial amino acid conservation in protein-protein hetero complexes

A long-standing question in molecular biology is whether interfaces of protein-protein complexes are more conserved than the rest of the protein surfaces. Although it has been reported that conservation can be used as an indicator for predicting interaction sites on proteins, there are recent reports stating that the interface regions are only slightly more conserved than the rest of the protein surfaces, with conservation signals not being statistically significant enough for predicting protein-protein binding sites. In order to properly address these controversial reports we have studied a set of 28 well resolved hetero complex structures of proteins that consists of transient and non-transient complexes. The surface positions were classified into four conservation classes and the conservation index of the surface positions was quantitatively analyzed. The results indicate that the surface density of highly conserved positions is significantly higher in the protein-protein interface regions compared with the other regions of the protein surface. However, the average conservation index of the patches in the interface region is not significantly higher compared with other surface regions of the protein structures. This finding demonstrates that the number of conserved residue positions is a more appropriate indicator for predicting protein-protein binding sites than the average conservation index in the interacting region. We have further validated our findings on a set of 59 benchmark complex structures. Furthermore, an analysis of 19 complexes of antigen-antibody interactions shows that there is no conservation of amino acid positions in the interacting regions of these complexes, as expected, with the variable region of the immunoglobulins interacting mostly with the antigens. Interestingly, antigen interacting regions also have a higher number of non-conserved residue positions in the interacting region than the rest of the protein surface.     

A protein domain-based interactome network for C. elegans early embryogenesis

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.     

Probing the structure and function of human glutaminase-interacting protein: a possible target for drug design

PDZ domains are one of the most ubiquitous protein-protein interaction modules found in living systems. Glutaminase interacting protein (GIP), also known as Tax interacting protein 1 (TIP-1), is a PDZ domain-containing protein, which plays pivotal roles in many aspects of cellular signaling, protein scaffolding and modulation of tumor growth. We report here the overexpression, efficient refolding, single-step purification, and biophysical characterization of recombinant human GIP with three different C-terminal target protein recognition sequence motifs by CD, fluorescence, and high-resolution solution NMR methods. It is clear from our NMR analysis that GIP contains 2 alpha-helices and 6 beta-strands. The three target protein C-terminal recognition motifs employed in our interaction studies are glutaminase, beta-catenin and FAS. This is the first report of GIP recognition of the cell surface protein FAS, which belongs to the tumor necrosis factor (TNF) receptor family and mediates cell apoptosis. The dissociation constant ( K D) values for the binding of GIP with different interacting partners as measured by fluorescence spectroscopy range from 1.66 to 2.64 microM. Significant chemical shift perturbations were observed upon titration of GIP with above three ligands as monitored by 2D {(1)H, (15)N}-HSQC NMR spectroscopy. GIP undergoes a conformational change upon ligand binding.     

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells.BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.     

Bacterial beta-lactamase fragmentation complementation strategy can be used as a method for identifying interacting protein pairs

We investigated the applicability of the TEM-1 beta- lactamase fragment complementation (BFC) system to develop a strategy for the screening of protein-protein interactions in bacteria. A BFC system containing a human Fas-associated death domain (hFADD) and human Fas death domain (hFasDD) was generated. The hFADD-hFasDD interaction was verified by cell survivability in ampicillin-containing medium and the colorimetric change of nitrocefin. It was also confirmed by His pull-down assay using cell lysates obtained in selection steps. A coiled-coil helix coiled-coil domain-containing protein 5 (CHCH5) was identified as an interacting protein of human uracil DNA glycosylase (hUNG) from the bacterial BFC cDNA library strategy. The interaction between hUNG and CHCH5 was further confirmed with immunoprecipitation using a mammalian expression system. CHCH5 enhanced the DNA glycosylase activity of hUNG to remove uracil from DNA duplexes containing a U/G mismatch pair. These results suggest that the bacterial BFC cDNA library strategy can be effectively used to identify interacting protein pairs.     

The Structural Pathway of Interleukin 1 (IL-1) Initiated Signaling Reveals Mechanisms of Oncogenic Mutations and SNPs in Inflammation and Cancer

Interleukin-1 (IL-1) is a large cytokine family closely related to innate immunity and inflammation. IL-1 proteins are key players in signaling pathways such as apoptosis, TLR, MAPK, NLR and NF-κB. The IL-1 pathway is also associated with cancer, and chronic inflammation increases the risk of tumor development via oncogenic mutations. Here we illustrate that the structures of interfaces between proteins in this pathway bearing the mutations may reveal how. Proteins are frequently regulated via their interactions, which can turn them ON or OFF. We show that oncogenic mutations are significantly at or adjoining interface regions, and can abolish (or enhance) the protein-protein interaction, making the protein constitutively active (or inactive, if it is a repressor). We combine known structures of protein-protein complexes and those that we have predicted for the IL-1 pathway, and integrate them with literature information. In the reconstructed pathway there are 104 interactions between proteins whose three dimensional structures are experimentally identified; only 15 have experimentally-determined structures of the interacting complexes. By predicting the protein-protein complexes throughout the pathway via the PRISM algorithm, the structural coverage increases from 15% to 71%. In silico mutagenesis and comparison of the predicted binding energies reveal the mechanisms of how oncogenic and single nucleotide polymorphism (SNP) mutations can abrogate the interactions or increase the binding affinity of the mutant to the native partner. Computational mapping of mutations on the interface of the predicted complexes may constitute a powerful strategy to explain the mechanisms of activation/inhibition. It can also help explain how an oncogenic mutation or SNP works.     

Mapping of protein-protein interaction sites by the 'absence of interference' approach

Protein-protein interactions are critical to most biological processes, and locating protein-protein interfaces on protein structures is an important task in molecular biology. We developed a new experimental strategy called the ‘absence of interference’ approach to determine surface residues involved in protein-protein interaction of established yeast two-hybrid pairs of interacting proteins. One of the proteins is subjected to high-level randomization by error-prone PCR. The resulting library is selected by yeast two-hybrid system for interacting clones that are isolated and sequenced. The interaction region can be identified by an absence or depletion of mutations. For data analysis and presentation, we developed a Web interface that analyzes the mutational spectrum and displays the mutational frequency on the surface of the structure (or a structural model) of the randomized protein†. Additionally, this interface might be of use for the display of mutational distributions determined by other types of random mutagenesis experiments. We applied the approach to map the interface of the catalytic domain of the DNA methyltransferase Dnmt3a with its regulatory factor Dnmt3L. Dnmt3a was randomized with high mutational load. A total of 76 interacting clones were isolated and sequenced, and 648 mutations were identified. The mutational pattern allowed to identify a unique interaction region on the surface of Dnmt3a, which comprises about 500-600 Å². The results were confirmed by site-directed mutagenesis and structural analysis. The absence-of-interference approach will allow high-throughput mapping of protein interaction sites suitable for functional studies and protein docking.     

Detecting mutually exclusive interactions in protein-protein interaction maps

Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.     

Recent advances in charting protein-protein interaction: mass spectrometry-based approaches

Cellular functions are the result of the coordinated action of groups of proteins interacting in molecular assemblies or pathways. The systematic and unbiased charting of protein-protein networks in a variety of organisms has become an important challenge in systems biology. These protein-protein interaction networks contribute comprehensive cartographies of key pathways or biological processes relevant to health or disease by providing a molecular frame for the interpretation of genetic links. At a structural level protein-protein networks enabled the identification of the sequences, motifs and structural folds involved in the process of molecular recognition. A rapidly growing choice of technologies is available for the global charting of protein-protein interactions. In this review, we focus on recent developments in a suite of methods that enable the purification of protein complexes under native conditions and, in conjunction with protein mass spectrometry, identification of their constituents.     

Studies on the inference of protein binding regions across fold space based on structural similarities

The emerging picture of a continuous protein fold space highlights the existence of non obvious structural similarities between proteins with apparent different topologies. The identification of structure resemblances across fold space and the analysis of similar recognition regions may be a valuable source of information towards protein structure-based functional characterization. In this work, we use non-sequential structural alignment methods (ns-SAs) to identify structural similarities between protein pairs independently of their SCOP hierarchy, and we calculate the significance of binding region conservation using the interacting residues overlap in the ns-SA. We cluster the binding inferences for each family to distinguish already known family binding regions from putative new ones. Our methodology exploits the enormous amount of data available in the PDB to identify binding region similarities within protein families and to propose putative binding regions. Our results indicate that there is a plethora of structurally common binding regions among proteins, independently of current fold classifications. We obtain a 6- to 8-fold enrichment of novel binding regions, and identify binding inferences for 728 protein families that so far lack binding information in the PDB. We explore binding mode analogies between ligands from commonly clustered binding regions to investigate the utility of our methodology. A comprehensive analysis of the obtained binding inferences may help in the functional characterization of protein recognition and assist rational engineering. The data obtained in this work is available in the download link at www.scowlp.org.     

Mapping the topology and determination of a low-resolution three-dimensional structure of the calmodulin-melittin complex by chemical cross-linking and high-resolution FTICRMS: direct demonstration of multiple binding modes

Calmodulin serves as a calcium-dependent regulator in many metabolic pathways and is known to bind with high affinity to various target proteins and peptides. One such target is the small peptide melittin, the principal component of honeybee venom. The calmodulin-melittin system was used as a model system to gain further insight into target recognition of calmodulin. Using chemical cross-linking in combination with high-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS), we have determined the interacting regions within the calcium-dependent calmodulin-melittin complex and thus the orientation of bound melittin. Using ambiguous distance restraints derived from the chemical cross-linking data in combination with recently developed computational methods of conjoined rigid body/torsion angle simulated annealing, we were able to generate low-resolution three-dimensional structure models of the calmodulin-melittin complex, for which no high-resolution structure exists to date. Our data provide evidence for the first time that calmodulin can recognize target peptides in two opposing orientations simultaneously. The general procedure for mapping interacting regions within the complex involves conjugation of calmodulin and melittin with several cross-linking reagents possessing different specificities and spacer lengths, followed by enzymatic proteolysis of the cross-linked complex. The highly complex peptide mixtures were subsequently analyzed by nano-HPLC, which was online coupled to a FTICR mass spectrometer equipped with a nano-electrospray ionization source. The mass spectra obtained in this manner were screened for possible cross-linking products using customized software programs. This integrated approach, exemplified for mapping the topology of the calmodulin-melittin complex, is likely to have wide-ranging implications for structural studies on protein-protein interactions.     

Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.     

Taking advantage of local structure descriptors to analyze interresidue contacts in protein structures and protein complexes

Interresidue protein contacts in proteins structures and at protein-protein interface are classically described by the amino acid types of interacting residues and the local structural context of the contact, if any, is described using secondary structures. In this study, we present an alternate analysis of interresidue contact using local structures defined by the structural alphabet introduced by Camproux et al. This structural alphabet allows to describe a 3D structure as a sequence of prototype fragments called structural letters, of 27 different types. Each residue can then be assigned to a particular local structure, even in loop regions. The analysis of interresidue contacts within protein structures defined using Vorono? tessellations reveals that pairwise contact specificity is greater in terms of structural letters than amino acids. Using a simple heuristic based on specificity score comparison, we find that 74% of the long-range contacts within protein structures are better described using structural letters than amino acid types. The investigation is extended to a set of protein-protein complexes, showing that the similar global rules apply as for intraprotein contacts, with 64% of the interprotein contacts best described by local structures. We then present an evaluation of pairing functions integrating structural letters to decoy scoring and show that some complexes could benefit from the use of structural letter-based pairing functions.     

Does structural and chemical divergence play a role in precluding undesirable protein interactions?

To understand the evolutionary forces establishing, maintaining, breaking, or precluding protein-protein interactions, a comprehensive data set of protein complexes has been analyzed to examine the overlap between protein interfaces and the most conserved or divergent protein surface areas. The most divergent areas tend to be found predominantly away from protein interfaces, although when found at interfaces, they are associated with specific lack of cross-reactivity between close homologues, like in antibody-antigen complexes. Moreover, the amino acid composition of highly variable regions is significantly different from any other protein surfaces. The variable regions present higher structural plasticity as a result of insertions and deletions, and favor charged over hydrophobic residues, a known strategy to minimize aggregation. This suggests that (1) a rapid rate of mutations at these regions might be continuously altering their properties, making difficult the coadaptation, in shape and chemical complementarity, to potential interacting partners; and (2) the existence of some form of selective pressure for variable areas away from interfaces to accumulate charged residues, perhaps as an evolutionary mechanism to increase solubility and minimize undesirable interactions within the crowded cellular environment. Finally, these results are placed into the context of the aberrant oligomerization of sickle-cell anemia hemoglobin and prion proteins.